Report on Exact and Statistical Matching Techniques
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Introduction to Social Statistics
SOCY 3400: INTRODUCTION TO SOCIAL STATISTICS MWF 11-12:00; Lab PGH 492 (sec. 13748) M 2-4 or (sec. 13749)W 2-4 Professor: Jarron M. Saint Onge, Ph.D. Office: PGH 489 Phone: (713) 743-3962 Email: [email protected] Office Hours: MW 10-11 (Please email) or by appointment Teaching Assistant: TA Email: Office Hours: TTh 10-12 or by appointment Required Text: McLendon, M. K. 2004. Statistical Analysis in the Social Sciences. Additional materials will be distributed through WebCT COURSE DESCRIPTION: Sociological research relies on experience with both qualitative (e.g. interviews, participant observation) and quantitative methods (e.g., statistical analyses) to investigate social phenomena. This class focuses on learning quantitative methods for furthering our knowledge about the world around us. This class will help students in the social sciences to gain a basic understanding of statistics, whether to understand, critique, or conduct social research. The course is divided into three main sections: (1) Descriptive Statistics; (2) Inferential Statistics; and (3) Applied Techniques. Descriptive statistics will allow you to summarize and describe data. Inferential Statistics will allow you to make estimates about a population (e.g., this entire class) based on a sample (e.g., 10 or 12 students in the class). The third section of the course will help you understand and interpret commonly used social science techniques that will help you to understand sociological research. In this class, you will learn concepts associated with social statistics. You will learn to understand and grasp the concepts, rather than only focusing on getting the correct answers. -
Publications Using the HMD in Years 1997 – 2013
Publications Using the HMD in Years 1997 – 2013 Table of Contents Official Reports ................................................................................................................................................... 1 Books and Book Chapters .............................................................................................................................. 4 Journal Articles ................................................................................................................................................ 12 Dissertations and Theses .............................................................................................................................. 63 Technical Reports, Working, Research and Discussion Papers ......................................................... 67 Introduction The following comprises a list of publications that rely on data from the Human Mortality Database. It resorts to the Google Scholar web search engine1 using “Human mortality database” and “Berkeley mortality database” as the search expressions. The expressions may appear anywhere in the publication (title, abstract, body). Works that used the BMD are identified by “[BMD]” at the end of the citation; all other publications used the HMD. This version of the HMD reference list concentrates on scholarly articles and books, dissertations, technical reports and working papers published from January 1997 up to the end of November 2013. The list also includes all publications by the HMD team members based on analyses of -
Statistical Matching: a Paradigm for Assessing the Uncertainty in the Procedure
Journal of Of®cial Statistics, Vol. 17, No. 3, 2001, pp. 407±422 Statistical Matching: A Paradigm for Assessing the Uncertainty in the Procedure Chris Moriarity1 and Fritz Scheuren2 Statistical matching has been widely used by practitioners without always adequate theoreti- cal underpinnings. The work of Kadane (1978) has been a notable exception and the present article extends his insights. Kadane's 1978 article is reprinted in this JOS issue. Modern com- puting can make possible, under techniques described here, a real advance in the application of statistical matching. Key words: Multivariate normal; complex survey designs; robustness; resampling; variance- covariance structures; and application suggestions. 1. Introduction Many government policy questions, whether on the expenditure or tax side, lend them- selves to microsimulation modeling, where ``what if'' analyses of alternative policy options are carried out (e.g., Citro and Hanushek 1991). Often, the starting point for such models, in an attempt to achieve a degree of verisimilitude, is to employ information contained in several survey microdata ®les. Typically, not all the variables wanted for the modeling have been collected together from a single individual or family. However, the separate survey ®les may have many demographic and other control variables in common. The idea arose, then, of matching the separate ®les on these common variables and thus creating a composite ®le for analysis. ``Statistical matching,'' as the technique began to be called, has been more or less widely practiced since the advent of public use ®les in the 1960's. Arguably, the desire to employ statistical matching was even an impetus for the release of several of the early public use ®les, including those involving U.S. -
Statistics on Spotlight: World Statistics Day 2015
Statistics on Spotlight: World Statistics Day 2015 Shahjahan Khan Professor of Statistics School of Agricultural, Computational and Environmental Sciences University of Southern Queensland, Toowoomba, Queensland, AUSTRALIA Founding Chief Editor, Journal of Applied Probability and Statistics (JAPS), USA Email: [email protected] Abstract In the age of evidence based decision making and data science, statistics has become an integral part of almost all spheres of modern life. It is being increasingly applied for both private and public benefits including business and trade as well as various public sectors, not to mention its crucial role in research and innovative technologies. No modern government could conduct its normal functions and deliver its services and implement its development agenda without relying on good quality statistics. The key role of statistics is more visible and engraved in the planning and development of every successful nation state. In fact, the use of statistics is not only national but also regional, international and transnational for organisations and agencies that are driving social, economic, environmental, health, poverty elimination, education and other agendas for planned development. Starting from stocktaking of the state of the health of various sectors of the economy of any nation/region to setting development goals, assessment of progress, monitoring programs and undertaking follow-up initiatives depend heavily on relevant statistics. Only statistical methods are capable of determining indicators, comparing them, and help identify the ways to ensure balanced and equitable development. 1 Introduction The goals of the celebration of World Statistics Day 2015 is to highlight the fact that official statistics help decision makers develop informed policies that impact millions of people. -
A Machine Learning Approach to Census Record Linking∗
A Machine Learning Approach to Census Record Linking∗ James J. Feigenbaumy March 28, 2016 Abstract Thanks to the availability of new historical census sources and advances in record linking technology, economic historians are becoming big data geneal- ogists. Linking individuals over time and between databases has opened up new avenues for research into intergenerational mobility, the long run effects of early life conditions, assimilation, discrimination, and the returns to edu- cation. To take advantage of these new research opportunities, scholars need to be able to accurately and efficiently match historical records and produce an unbiased dataset of links for analysis. I detail a standard and transparent census matching technique for constructing linked samples that can be repli- cated across a variety of cases. The procedure applies insights from machine learning classification and text comparison to record linkage of historical data. My method teaches an algorithm to replicate how a well trained and consistent researcher would create a linked sample across sources. I begin by extracting a subset of possible matches for each record, and then use training data to tune a matching algorithm that attempts to minimize both false positives and false negatives, taking into account the inherent noise in historical records. To make the procedure precise, I trace its application to an example from my own work, linking children from the 1915 Iowa State Census to their adult-selves in the 1940 Federal Census. In addition, I provide guidance on a number of practical questions, including how large the training data needs to be relative to the sample. ∗I thank Christoph Hafemeister, Jamie Lee, Christopher Muller, Martin Rotemberg, and Nicolas Ziebarth for detailed feedback and thoughts on this project, as well as seminar participants at the NBER DAE Summer Institute and the Berkeley Demography Conference on Census Linking. -
THE HISTORY and DEVELOPMENT of STATISTICS in BELGIUM by Dr
THE HISTORY AND DEVELOPMENT OF STATISTICS IN BELGIUM By Dr. Armand Julin Director-General of the Belgian Labor Bureau, Member of the International Statistical Institute Chapter I. Historical Survey A vigorous interest in statistical researches has been both created and facilitated in Belgium by her restricted terri- tory, very dense population, prosperous agriculture, and the variety and vitality of her manufacturing interests. Nor need it surprise us that the successive governments of Bel- gium have given statistics a prominent place in their affairs. Baron de Reiffenberg, who published a bibliography of the ancient statistics of Belgium,* has given a long list of docu- ments relating to the population, agriculture, industry, commerce, transportation facilities, finance, army, etc. It was, however, chiefly the Austrian government which in- creased the number of such investigations and reports. The royal archives are filled to overflowing with documents from that period of our history and their very over-abun- dance forms even for the historian a most diflScult task.f With the French domination (1794-1814), the interest for statistics did not diminish. Lucien Bonaparte, Minister of the Interior from 1799-1800, organized in France the first Bureau of Statistics, while his successor, Chaptal, undertook to compile the statistics of the departments. As far as Belgium is concerned, there were published in Paris seven statistical memoirs prepared under the direction of the prefects. An eighth issue was not finished and a ninth one * Nouveaux mimoires de I'Acadimie royale des sciences et belles lettres de Bruxelles, t. VII. t The Archives of the kingdom and the catalogue of the van Hulthem library, preserved in the Biblioth^que Royale at Brussells, offer valuable information on this head. -
Precept 8: Some Review, Heteroskedasticity, and Causal Inference Soc 500: Applied Social Statistics
Precept 8: Some review, heteroskedasticity, and causal inference Soc 500: Applied Social Statistics Alex Kindel Princeton University November 15, 2018 Alex Kindel (Princeton) Precept 8 November 15, 2018 1 / 27 Learning Objectives 1 Review 1 Calculating error variance 2 Interaction terms (common support, main effects) 3 Model interpretation ("increase", "intuitively") 4 Heteroskedasticity 2 Causal inference with potential outcomes 0Thanks to Ian Lundberg and Xinyi Duan for material and ideas. Alex Kindel (Princeton) Precept 8 November 15, 2018 2 / 27 Calculating error variance We have some data: Y, X, Z. We think the correct model is Y = X + Z + u. We estimate this conditional expectation using OLS: Y = β0 + β1X + β2Z We want to know the standard error of β1. Standard error of β1 r ^2 ^ 1 σu 2 2 SE(βj ) = 2 Pn 2 , where Rj is the R of a regression of 1−Rj i=1(xij −x¯j ) variable j on all others. ^2 Question: What is σu? Alex Kindel (Princeton) Precept 8 November 15, 2018 3 / 27 Calculating error variance P 2 ^2 i u^i σu = DFresid You can adjust this in finite samples by u¯^ (why?) Alex Kindel (Princeton) Precept 8 November 15, 2018 4 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Assume X ∼ N (?; ?) and Z 2 f0; 1g Alex Kindel (Princeton) Precept 8 November 15, 2018 5 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Scenario 1 When Z = 0, X ∼ N (3; 4) When Z = 1, X ∼ N (−3; 2) Do you think an interaction term is justified here? Alex Kindel (Princeton) Precept 8 November 15, 2018 6 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Scenario -
Santa ARRIVES
CELEBRITY CHRISTMAS LIGHT SWITCH ON & Laser SHOW Late Night Santa ARRIVES Shopping BY BOAT AT THE HARBOUR BUSES & FREE PARKING Follow him TO BOYES’ GROTTO FAMILY FUN Treasure Trail Victorian MAGIC Christmas THEATRE Market SANTA SPECIALS RAILWAY FUN RUN Scarborough Sparkle STREET ENTERTAINMENT ICE SCULPTURE MUSIC NOVEMBER DECEMBER 14 Nov CELEBRITY LIGHT SWITCH ON from 4pm. Laser light display, 01 Dec ICE SCULPTURE TOWN CENTRE panto & christmas show characters, live music Spa panto acts, Frozen characters and pirates. 14 Nov LATE NIGHT SHOPPING starts tonight! TOWN CENTRE Spot prize competition for winning costumes between 16 Nov SANTA ARRIVES by boat into the harbour then 11am and 4pm, and Scarborough Fair Collection Decap Organ makes his way along Eastborough to Boyes 01 Dec MAGIC MIKE SCARBOROUGH SPA 16 – 17 Nov A CHRISTMAS CAROUSEL YMCA 02 Dec – 03 Dec ROCKIN CAROLS YMCA MARAUDING PIRATE CREW TOWN CENTRE 05 Dec LATE NIGHT SHOPPING TOWN CENTRE 21 Nov LATE NIGHT SHOPPING TOWN CENTRE Treasure Trail Draw 7pm, The Naughty Christmas Entertainment from Julie Hatton School of Dance, Tree Entertainment, Marauding Pirate Crew, Magic Mike Scarborough Fair Collection Decap Organ, 05 Dec – 29 Dec TREASURE ISLAND STEPHEN JOSEPH THEATRE Marauding Pirate Crew, Treasure Trail Draw 7pm 07 Dec CHILDREN’S CHRISTMAS CRAFTS FREE SCARBOROUGH LIBRARY 23 Nov GET READY FOR CHRISTMAS FAIR SCARBOROUGH MARKET HALL & VAULTS 07 Dec SALVATION ARMY TOWN CENTRE 23 Nov LANTERN MAKING LIBRARY 07 – 08 Dec MARAUDING PIRATE CREW TOWN CENTRE SCARBOROUGH JUNIOR CONCERT BAND -
A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol
A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol. 43, No. 3, pp. 253–278 | ©2011 ISTE | iste.org A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research Karen Larwin Youngstown State University David Larwin Kent State University at Salem Abstract The present meta-analysis is a comprehensive investigation of the effectiveness of computer-assisted instruction (CAI) on student achievement in postsec- ondary statistics education across a forty year period of time. The researchers calculated an overall effect size of 0.566 from 70 studies, for a total of 219 effect-size measures from a sample of n = 40,125 participants. These results suggest that the typical student moved from the 50th percentile to the 73rd percentile when technology was used as part of the curriculum. This study demonstrates that subcategories can further the understanding of how the use of CAI in statistics education might be maximized. The study discusses im- plications and limitations. (Keywords: statistics education, computer-assisted instruction, meta-analysis) iscovering how students learn most effectively is one of the major goals of research in education. During the last 30 years, many re- Dsearchers and educators have called for reform in the area of statistics education in an effort to more successfully reach the growing population of students, across an expansive variety of disciplines, who are required to complete coursework in statistics (e.g., Cobb, 1993, 2007; Garfield, 1993, 1995, 2002; Giraud, 1997; Hogg, 1991; Lindsay, Kettering, & Siegmund, 2004; Moore, 1997; Roiter, & Petocz, 1996; Snee, 1993;Yilmaz, 1996). -
Stability and Median Rationalizability for Aggregate Matchings
games Article Stability and Median Rationalizability for Aggregate Matchings Federico Echenique 1, SangMok Lee 2, Matthew Shum 1 and M. Bumin Yenmez 3,* 1 Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA; [email protected] (F.E.); [email protected] (M.S.) 2 Department of Economics, Washington University in St. Louis, St. Louis, MO 63130, USA; [email protected] 3 Department of Economics, Boston College, Chestnut Hill, MA 02467, USA * Correspondence: [email protected] Abstract: We develop the theory of stability for aggregate matchings used in empirical studies and establish fundamental properties of stable matchings including the result that the set of stable matchings is a non-empty, complete, and distributive lattice. Aggregate matchings are relevant as matching data in revealed preference theory. We present a result on rationalizing a matching data as the median stable matching. Keywords: aggregate matching; median stable matching; rationalizability; lattice 1. Introduction Following the seminal work of [1], an extensive literature has developed regarding matching markets with non-transferable utility. This literature assumes that there are agent-specific preferences, and studies the existence of stable matchings in which each Citation: Echenique, F.; Lee, S.; agent prefers her assigned partner to the outside option of being unmatched, and there Shum, M.; Yenmez, M.B. Stability and are no pairs of agents that would like to match with each other rather than keeping their Median Rationalizability for assigned partners. Aggregate Matchings. Games 2021, 12, In this paper, we develop the theory of stability for aggregate matchings, which we 33. -
Appendix 2: Publications and Other Works Using Data from the Human
Publications Using the HMD or BMD Table of Contents Introduction.............................................................................................................................................1 Official Reports.......................................................................................................................................1 Books and Book Chapters......................................................................................................................2 Journal Articles.......................................................................................................................................8 Dissertations and Theses.....................................................................................................................27 Technical Reports and Working Papers...............................................................................................29 Introduction The following comprises a list of publications that rely on data from the Human Mortality Database (HMD) or the Berkeley Mortality Database (BMD). Works that used the BMD are identified by “[BMD]” at the end of the citation; all other publications used the HMD. This list is probably not a complete list of all publications based on the HMD, as there may be others that remain unknown to us. The publications are grouped into six categories: i) official reports, ii) books and book chapters, iii) journal articles, iv) dissertations and theses, v) technical reports and working papers. Official Reports 1. Balkwill, -
Full Property Address Primary Liable
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